Abstract: The advances of Micro-electromechanical systems (MEMS) technology lead to new types of sensors named “multimodal” sensors where multiple features can be sensed and reported by one sensor. Forming a wireless sensor network of such sensors poses new challenges to the wireless sensor networks in addition to the current challenges. Currently, each multimodal sensor reports periodically a message for each feature or a long message that contains all the features compared to the traditional sensors. Such multimodal sensor networks could be used for multiple purposes and serve different applications. However, data handling and information processing as well as data/decision tasks became much harder than before. In this paper, we introduce a set of clustering algorithms taking into consideration the reported multiple features as well as some of the sensors parameters such as nodes' residual energy and clusterheads' degree. The paper utilizes different clustering techniques including fuzzy logic. The proposed algorithms are designed to simplify the next step operation which is data/decision fusion and decision making operations. Through an extensive set of experiments, the proposed algorithms are evaluated.